Sources and Secondary Production of Organic Aerosols in the Northeastern United States during WINTER

Most intensive field studies investigating aerosols have been conducted in summer, and thus, wintertime aerosol sources and chemistry are comparatively poorly understood. An aerosol mass spectrometer was flown on the National Science Foundation/National Center for Atmospheric Research C‐130 during the Wintertime INvestigation of Transport, Emissions, and Reactivity (WINTER) 2015 campaign in the northeast United States. The fraction of boundary layer submicron aerosol that was organic aerosol (OA) was about a factor of 2 smaller than during a 2011 summertime study in a similar region. However, the OA measured in WINTER was almost as oxidized as OA measured in several other studies in warmer months of the year. Fifty‐eight percent of the OA was oxygenated (secondary), and 42% was primary (POA). Biomass burning OA (likely from residential heating) was ubiquitous and accounted for 33% of the OA mass. Using nonvolatile POA, one of two default secondary OA (SOA) formulations in GEOS‐Chem (v10‐01) shows very large underpredictions of SOA and O/C (5×) and overprediction of POA (2×). We strongly recommend against using that formulation in future studies. Semivolatile POA, an alternative default in GEOS‐Chem, or a simplified parameterization (SIMPLE) were closer to the observations, although still with substantial differences. A case study of urban outflow from metropolitan New York City showed a consistent amount and normalized rate of added OA mass (due to SOA formation) compared to summer studies, although proceeding more slowly due to lower OH concentrations. A box model and SIMPLE perform similarly for WINTER as for Los Angeles, with an underprediction at ages \u3c6 hr, suggesting that fast chemistry might be missing from the models

Anthropogenic Control over Wintertime Oxidation of Atmospheric Pollutants

Anthropogenic air pollutants such as nitrogen oxides (NO(x) = NO + NO(2)), sulfur dioxide (SO(2)), and volatile organic compounds (VOC), among others, are emitted to the atmosphere throughout the year from energy production and use, transportation, and agriculture. These primary pollutants lead to the formation of secondary pollutants such as fine particulate matter (PM(2.5)) and ozone (O(3)) and perturbations to the abundance and lifetimes of short-lived greenhouse gases. Free radical oxidation reactions driven by solar radiation govern the atmospheric lifetimes and transformations of most primary pollutants and thus their spatial distributions. During winter in the mid and high latitudes, where a large fraction of atmospheric pollutants are emitted globally, such photochemical oxidation is significantly slower. Using observations from a highly instrumented aircraft, we show that multi-phase reactions between gas-phase NO(x) reservoirs and aerosol particles, as well as VOC emissions from anthropogenic activities, lead to a suite of atypical radical precursors dominating the oxidizing capacity in polluted winter air, and thus, the distribution and fate of primary pollutants on a regional to global scale

R|S Atlas: Identifying Existing Cohort Study Data Resources to Accelerate Epidemiological Research on the Influence of Religion and Spirituality on Human Health

OBJECTIVE: Many studies have documented significant associations between religion and spirituality (R/S) and health, but relatively few prospective analyses exist that can support causal inferences. To date, there has been no systematic analysis of R/S survey items collected in US cohort studies. We conducted a systematic content analysis of all surveys ever fielded in 20 diverse US cohort studies funded by the National Institutes of Health (NIH) to identify all R/S-related items collected from each cohort\u27s baseline survey through 2014. DESIGN: An R|S Ontology was developed from our systematic content analysis to categorise all R/S survey items identified into key conceptual categories. A systematic literature review was completed for each R/S item to identify any cohort publications involving these items through 2018. RESULTS: Our content analysis identified 319 R/S survey items, reflecting 213 unique R/S constructs and 50 R|S Ontology categories. 193 of the 319 extant R/S survey items had been analysed in at least one published paper. Using these data, we created the R|S Atlas (https://atlas.mgh.harvard.edu/), a publicly available, online relational database that allows investigators to identify R/S survey items that have been collected by US cohorts, and to further refine searches by other key data available in cohorts that may be necessary for a given study (eg, race/ethnicity, availability of DNA or geocoded data). CONCLUSIONS: R|S Atlas not only allows researchers to identify available sources of R/S data in cohort studies but will also assist in identifying novel research questions that have yet to be explored within the context of US cohort studies

The ACOS CO_2 retrieval algorithm – Part 1: Description and validation against synthetic observations

This work describes the NASA Atmospheric CO_2 Observations from Space (ACOS) X_(CO_2) retrieval algorithm, and its performance on highly realistic, simulated observations. These tests, restricted to observations over land, are used to evaluate retrieval errors in the face of realistic clouds and aerosols, polarized non-Lambertian surfaces, imperfect meteorology, and uncorrelated instrument noise. We find that post-retrieval filters are essential to eliminate the poorest retrievals, which arise primarily due to imperfect cloud screening. The remaining retrievals have RMS errors of approximately 1 ppm. Modeled instrument noise, based on the Greenhouse Gases Observing SATellite (GOSAT) in-flight performance, accounts for less than half the total error in these retrievals. A small fraction of unfiltered clouds, particularly thin cirrus, lead to a small positive bias of ~0.3 ppm. Overall, systematic errors due to imperfect characterization of clouds and aerosols dominate the error budget, while errors due to other simplifying assumptions, in particular those related to the prior meteorological fields, appear small

The impact of neonatal breast-feeding on growth trajectories of youth exposed and unexposed to diabetes in utero: the EPOCH Study

ObjectiveTo evaluate the influence of breastfeeding on the body mass index (BMI) growth trajectory from birth through 13 years of age among offspring of diabetic pregnancies (ODP) and offspring of non-diabetic pregnancies (ONDP) participating in the EPOCH study.SubjectsThere were 94 ODP and 399 ONDP who had multiple BMI measures obtained from birth throughout childhood. A measure of breast milk-months was derived from maternal self-report to categorize breastfeeding status as adequate (≥6 breast milk-months) or low (<6 breast milk-months). Mixed linear effects models were constructed to assess the impact of breastfeeding on the BMI growth curves during infancy (birth to 27 months) and childhood (27 months to 13 years).ResultsODP who were adequately breastfed had a slower BMI growth trajectory during childhood (p=0.047) and slower period-specific growth velocity with significant differences between 4 to 6 years of age (p=0.03) and 6 to 9 years of age (p=0.01) compared to ODP with low breastfeeding. A similar pattern was seen in the ONDP, with adequate breastfeeding associated with lower average BMI in infancy (p=0.03) and childhood (p=0.0002) and a slower growth trajectory in childhood (p=0.0002). Slower period-specific growth velocity was seen among the ONDP associated with adequate breastfeeding with significant differences between 12–26 months (p=0.02), 4–6 years (p=0.03), 6–9 years (p=0.0001) and 9–13 years of age (p<.0001).ConclusionOur study provides novel evidence that breastfeeding is associated with long-term effects on childhood BMI growth that extend beyond infancy into early and late childhood. Importantly, these effects are also present in the high-risk offspring, exposed to overnutrition during pregnancy. Breastfeeding in the early postnatal period may represent a critical opportunity to reduce the risk of childhood obesity

Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm

Since September 2014, NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite has been taking measurements of reflected solar spectra and using them to infer atmospheric carbon dioxide levels. This work provides details of the OCO-2 retrieval algorithm, versions 7 and 8, used to derive the column-averaged dry air mole fraction of atmospheric CO2 (XCO2) for the roughly 100&thinsp;000 cloud-free measurements recorded by OCO-2 each day. The algorithm is based on the Atmospheric Carbon Observations from Space (ACOS) algorithm which has been applied to observations from the Greenhouse Gases Observing SATellite (GOSAT) since 2009, with modifications necessary for OCO-2. Because high accuracy, better than 0.25&thinsp;%, is required in order to accurately infer carbon sources and sinks from XCO2, significant errors and regional-scale biases in the measurements must be minimized. We discuss efforts to filter out poor-quality measurements, and correct the remaining good-quality measurements to minimize regional-scale biases. Updates to the radiance calibration and retrieval forward model in version 8 have improved many aspects of the retrieved data products. The version 8 data appear to have reduced regional-scale biases overall, and demonstrate a clear improvement over the version 7 data. In particular, error variance with respect to TCCON was reduced by 20&thinsp;% over land and 40&thinsp;% over ocean between versions 7 and 8, and nadir and glint observations over land are now more consistent. While this paper documents the significant improvements in the ACOS algorithm, it will continue to evolve and improve as the CO2 data record continues to expand.</p